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Record W1505635857 · doi:10.18438/b82s4t

Learners with Low Self-Efficacy for Information Literacy Rely on Library Resources Less Often But Are More Willing to Learn How to Use Them

2014· article· en· W1505635857 on OpenAlexvenueno aff
Dominique Daniel

Bibliographic record

VenueEvidence Based Library and Information Practice · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsInformation literacySelf-efficacyLikert scalePsychologyTest (biology)Medical educationLiteracyDescriptive statisticsMathematics educationScale (ratio)Information seekingComputer scienceSocial psychologyPedagogyMedicineLibrary scienceDevelopmental psychologyStatistics

Abstract

fetched live from OpenAlex

A Review of:
 Tang, Y., & Tseng, H. W. (2013). Distance learners’ self-efficacy and information literacy skills. The Journal of Academic Librarianship 39(6): 517-521. doi:10.1016/j.acalib.2013.08.008
 
 Abstract
 
 Objectives – To determine whether there is a relationship between self-efficacy (i.e., confidence) regarding information literacy skills and self-efficacy for distance learning; and to compare the use of electronic resources by high and low information literacy self-efficacy distance learners and their interest in learning more about searching.
 
 Design – Online survey.
 
 Setting – A small public university in the United States of America.
 
 Subjects – Undergraduate and graduate students enrolled in one or more online courses. Most respondents were in their twenties, 76% were female, 59% were undergraduates, and 69% were full time students.
 
 Methods – Students were asked six demographic questions, eight questions measuring their self-efficacy for information literacy, and four questions measuring their self-efficacy for online learning. All self-efficacy questions were adapted from previous studies and used a one to five Likert scale. The response rate was 6.2%. Correlational analysis was conducted to test the first two hypotheses (students who have higher self-efficacy for information seeking are more likely to have higher self-efficacy for online learning and for information manipulation). Descriptive analysis was used for the remaining hypotheses, to test whether students who have higher information literacy self-efficacy are more likely to have high library skills (hypothesis three) and are more interested in learning about how to use library resources (hypothesis four). Among respondents high information literacy self-efficacy and low self-efficacy groups were distinguished, using the mean score of information literacy self-efficacy.
 
 Main Results – There was a significant correlation between self-efficacy for information seeking and self-efficacy for online learning (r = .27), as well as self-efficacy for information manipulation (r = .79). Students with high information seeking self-efficacy were more likely to use library databases (28.72%), while low self-efficacy respondents more often chose commercial search engines (30.98%). However those respondents were more likely to be interested in learning how to use library resources.
 
 Conclusion – Distance students with higher self-efficacy for information seeking and use also had higher self-efficacy for online learning. It is important to encourage such self-efficacy since studies have shown that it relates to better information literacy skills and a higher ability to be self-regulated learners. Confident learners process information, make effective decisions, and improve their learning more easily. Furthermore many respondents in this survey had little or false knowledge of how to use appropriate resources for their learning needs. This points to the need for effective library instruction. This study also shows that low self-efficacy students would like to have library instruction, especially to help them plan specific research assignments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.891
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0070.736
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.268
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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